We identify the top methods for network structure prediction by computing the area under the receiver operating characteristic (AUROC) and accuracy (ACC) row sum and median values for each of the 28 networks. We weight these variables at 5 different ranges:
for (sim in (1:28)){
AUC<-read.csv(paste0("sim",sim,"/Combined_Data_onlyAUC.csv"),row.names = 1)
ACC<-read.csv(paste0("sim",sim,"/Combined_Data_onlyACC.csv"),row.names = 1)
#calculate AUC only sums and medians
AUC_sum<-cbind(AUC$Method_name,rowSums(AUC[,-1])) %>% as.data.frame()
colnames(AUC_sum)<-c("Method_name","Sum_of_avg_across_patients")
AUC_sum$Sum_of_avg_across_patients<-as.numeric(AUC_sum$Sum_of_avg_across_patients)
AUC_median<-cbind(AUC$Method_name,apply(AUC[-1], 1, median)) %>% as.data.frame()
colnames(AUC_median)<-c("Method_name","Median_across_patients")
write.csv(AUC_sum,paste0('sim',sim,'/Combined_data_sums_onlyAUC.csv'),quote=F)
write.csv(AUC_median,paste0('sim',sim,'/Combined_data_median_onlyAUC.csv'),quote=F)
#calculate ACC only sums and medians
ACC_sum<-cbind(ACC$Method_name,rowSums(ACC[,-1])) %>% as.data.frame()
colnames(ACC_sum)<-c("Method_name","Sum_of_avg_across_patients")
ACC_sum$Sum_of_avg_across_patients<-as.numeric(ACC_sum$Sum_of_avg_across_patients)
ACC_median<-cbind(ACC$Method_name,apply(ACC[-1], 1, median)) %>% as.data.frame()
colnames(ACC_median)<-c("Method_name","Median_across_patients")
write.csv(ACC_sum,paste0('sim',sim,'/Combined_data_sums_onlyACC.csv'),quote=F)
write.csv(ACC_median,paste0('sim',sim,'/Combined_data_median_onlyACC.csv'),quote=F)
# calculate equal weights sums and medians
AUC_half<-cbind(sapply(AUC[,-1],"*",0.5))
ACC_half<-cbind(sapply(ACC[,-1],"*",0.5))
equal<-cbind( AUC_half + ACC_half)
equal_sum<-cbind(AUC$Method_name,rowSums(equal)) %>% as.data.frame()
colnames(equal_sum)<-c("Method_name","Sum_of_avg_across_patients")
equal_sum$Sum_of_avg_across_patients<-as.numeric(equal_sum$Sum_of_avg_across_patients)
equal_median<-cbind(AUC$Method_name,apply(equal, 1, median)) %>% as.data.frame()
colnames(equal_median)<-c("Method_name","Median_across_patients")
write.csv(equal,paste0('sim',sim,'/Combined_data_equal.csv'),quote=F)
write.csv(equal_sum,paste0('sim',sim,'/Combined_data_sums_equal.csv'),quote=F)
write.csv(equal_median,paste0('sim',sim,'/Combined_data_median_equal.csv'),quote=F)
#calculate 0.3 AUROC and 0.7 ACC weights sums and medians
AUC_low<-cbind(sapply(AUC[,-1],"*",0.3))
ACC_high<-cbind(sapply(ACC[,-1],"*",0.7))
three_seven<-cbind( AUC_low + ACC_high)
three_seven_sum<-cbind(AUC$Method_name,rowSums(three_seven)) %>% as.data.frame()
colnames(three_seven_sum)<-c("Method_name","Sum_of_avg_across_patients")
three_seven_sum$Sum_of_avg_across_patients<-as.numeric(three_seven_sum$Sum_of_avg_across_patients)
three_seven_median<-cbind(AUC$Method_name,apply(three_seven, 1, median)) %>% as.data.frame()
colnames(three_seven_median)<-c("Method_name","Median_across_patients")
write.csv(three_seven,paste0('sim',sim,'/Combined_data_37.csv'),quote=F)
write.csv(three_seven_sum,paste0('sim',sim,'/Combined_data_sums_37.csv'),quote=F)
write.csv(three_seven_median,paste0('sim',sim,'/Combined_data_median_37.csv'),quote=F)
#calculate 0.7 AUROC and 0.3 ACC weights sums and medianssums and medians}
AUC_high<-cbind(sapply(AUC[,-1],"*",0.7))
ACC_low<-cbind(sapply(ACC[,-1],"*",0.3))
seven_three<-cbind( AUC_low + ACC_high)
seven_three_sum<-cbind(AUC$Method_name,rowSums(seven_three)) %>% as.data.frame()
colnames(seven_three_sum)<-c("Method_name","Sum_of_avg_across_patients")
seven_three_sum$Sum_of_avg_across_patients<-as.numeric(seven_three_sum$Sum_of_avg_across_patients)
seven_three_median<-cbind(AUC$Method_name,apply(seven_three, 1, median)) %>% as.data.frame()
colnames(seven_three_median)<-c("Method_name","Median_across_patients")
write.csv(seven_three,paste0('sim',sim,'/Combined_data_73.csv'),quote=F)
write.csv(seven_three_sum,paste0('sim',sim,'/Combined_data_sums_73.csv'),quote=F)
write.csv(seven_three_median,paste0('sim',sim,'/Combined_data_median_73.csv'),quote=F)
}Next, we compute the average Row Sums and Median values under the 5 AUROC and ACC weights.
patterns<-c("Combined_data_sums_onlyAUC","Combined_data_sums_onlyACC","Combined_data_sums_equal","Combined_data_sums_37","Combined_data_sums_73")
dir.create("averages")
for (pattern in patterns){
count_files <- list.files(recursive=TRUE,pattern=paste0("^",pattern,".csv"),full.names=TRUE)
d1<-data.frame()
for (i in count_files) {
# read file i as a data frame
f <- read.csv(i,row.names = 1)
if(length(d1) == 0){
d1 <- f
}else
{
d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients+f$Sum_of_avg_across_patients
}
}
d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients/28
d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients/50
d1<- d1 %>% arrange(desc(Sum_of_avg_across_patients))
write.csv(d1,paste0("averages/Average_",pattern,".csv"),quote=F)
}patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73")
for (pattern in patterns){
count_files <- list.files(recursive=TRUE,pattern=paste0("^",pattern,".csv"),full.names=TRUE)
count_files<-count_files[!grepl("all", count_files)]
d1<-data.frame()
for (i in count_files) {
# read file i as a data frame
f <- read.csv(i,row.names = 1)
if(length(d1) == 0){
d1 <- f
}else
{
d1$Median_across_patients<-d1$Median_across_patients+f$Median_across_patients
}
}
d1$Median_across_patients<-d1$Median_across_patients/28
d1<- d1 %>% arrange(desc(Median_across_patients))
write.csv(d1,paste0("averages/Average_Median_",pattern,".csv"),quote=F)
}
rm(list = ls())To identify the top performing network methods across all 28 networks and under different weights of the AUROC and ACC, we generate a heatmap with hierarchical clustering
pal<-rev(c("#FF0000","#0b5394","#38761d","#ffe700","#FFFFFF"))
count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE)
d1<-data.frame()
for (i in count_files){
f<-read.csv(i,row.names = 1)
if(length(d1) == 0){
d1<-f
}
else{
d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name")
}
}
rownames(d1)<-d1$Method_name
colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0")
d1<- d1 %>% select(-Method_name)
rowsums_top10<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:10]
rowsums_top4<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:4]
heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("Weight<sub>AUC=0.3</sub>,Weight<sub>ACC=0.7</sub>","Weight<sub>AUC=0.7</sub>,Weight<sub>ACC=0.3</sub>","Weight<sub>AUC=0.5</sub>,Weight<sub>ACC=0.5</sub>","Weight<sub>AUC=0</sub>,Weight<sub>ACC=1</sub>","Weight<sub>AUC=1</sub>,Weight<sub>ACC=0</sub>"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>%
layout(height = 900)patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73")
count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Median"),full.names=TRUE)
d1<-data.frame()
for (i in count_files){
f<-read.csv(i,row.names = 1)
if(length(d1) == 0){
d1<-f
}
else{
d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name")
}
}
rownames(d1)<-d1$Method_name
colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0")
d1<- d1 %>% select(-Method_name)
medians_top10<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:10]
medians_top4<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:4]
heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("Weight<sub>AUC=0.3</sub>,Weight<sub>ACC=0.7</sub>","Weight<sub>AUC=0.7</sub>,Weight<sub>ACC=0.3</sub>","Weight<sub>AUC=0.5</sub>,Weight<sub>ACC=0.5</sub>","Weight<sub>AUC=0</sub>,Weight<sub>ACC=1</sub>","Weight<sub>AUC=1</sub>,Weight<sub>ACC=0</sub>"),main="Average Median Value across 28 Networks",file = "heatmap_medians.pdf",width=800,height=1000,margins = c(40, 50)) %>%
layout(height = 900)These heatmaps show that the top methods across all networks with all weights of AUROC and ACC are BCohF, BCorrU, PCohF, and Tigress
We can plot how the individual top methods perform in all 28 networks individually using line plots under different weights of AUROC and ACC
create_rowsum_lineplot<-function(pattern){
count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE)
d1<-data.frame()
for (i in count_files) {
f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% rowsums_top4)
f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f))))
colnames(f)[3]<-c('time_series')
#if the counts object is empty just copy the f to m
if(length(d1) == 0){
d1 <- f
} else
{
#if the dataframe is not empty then merge the data
d1 <- rbind(d1,f)
}
}
d1$time_series<-gsub("sim","",d1$time_series)
d1$time_series<-str_pad(d1$time_series, 2, pad = "0")
colnames(d1)<-c("Method","Row Sum","Network")
d1$`Row Sum`<-d1$`Row Sum`/50
return(d1)
}create_median_lineplot<-function(pattern){
count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE)
d1<-data.frame()
for (i in count_files) {
f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% rowsums_top4)
f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f))))
colnames(f)[3]<-c('time_series')
#if the counts object is empty just copy the f to m
if(length(d1) == 0){
d1 <- f
} else
{
#if the dataframe is not empty then merge the data
d1 <- rbind(d1,f)
}
}
d1$time_series<-gsub("sim","",d1$time_series)
d1$time_series<-str_pad(d1$time_series, 2, pad = "0")
colnames(d1)<-c("Method","Median","Network")
return(d1)
}